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NVIDIA NCP-AAI Exam - Topic 4 Question 4 Discussion
Topic #: 4
In your RAG deployment, you've identified a performance bottleneck in the retrieval phase -- specifically, the time it takes to access the vector database.
Which of the following optimization strategies is most aligned with micro-service best practices, considering your RAG architecture?
The selected design maps to Introduce a dedicated service responsible solely for querying the vector database and returning relevant chunks, which is the highest-control path for this scenario rather than a prompt-only or single-service shortcut. For knowledge-grounded agents, the clean architecture is a RAG path with retrievers and vector indexes externalized from the LLM, then evaluated for retrieval quality and answer faithfulness. The agent should not infer operational details from latent model knowledge when it can bind to structured tools, retrievers, schemas, and examples. This reduces hallucinated endpoints, malformed parameters, stale facts, and brittle parsing when APIs, documents, or user inputs change. The distractors are weaker because they lean on A: Implement a cache-and-check mechanism where the retrieval microservice immediately returns the first...; B: Increase the size of the LLM model itself because it will automatically...; D: Optimize the LLM prompt to be shorter and more concise significantly reducing..., which compromises traceability, resilience, scalability, or policy enforcement in production. The answer therefore fits NVIDIA's production-agent pattern: modular workflow design, measurable runtime behavior, GPU-aware serving where applicable, and controlled integration with enterprise systems.
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